{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Searching in DARTS search space\n\nIn this tutorial, we demonstrate how to search in the famous model space proposed in `DARTS`_.\n\nThrough this process, you will learn:\n\n* How to use the built-in model spaces from NNI's model space hub.\n* How to use one-shot exploration strategies to explore a model space.\n* How to customize evaluators to achieve the best performance.\n\nIn the end, we get a strong-performing model on CIFAR-10 dataset, which achieves up to 97.28% accuracy.\n\n.. attention::\n\n   Running this tutorial requires a GPU.\n   If you don't have one, you can set ``gpus`` in :class:`~nni.nas.evaluator.pytorch.Classification` to be 0,\n   but do note that it will be much slower.\n\n\n## Use a pre-searched DARTS model\n\nSimilar to [the beginner tutorial of PyTorch](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html)_,\nwe begin with CIFAR-10 dataset, which is a image classification dataset of 10 categories.\nThe images in CIFAR-10 are of size 3x32x32, i.e., RGB-colored images of 32x32 pixels in size.\n\nWe first load the CIFAR-10 dataset with torchvision.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import nni\nimport torch\nfrom torchvision import transforms\nfrom torchvision.datasets import CIFAR10\nfrom nni.nas.evaluator.pytorch import DataLoader\n\nCIFAR_MEAN = [0.49139968, 0.48215827, 0.44653124]\nCIFAR_STD = [0.24703233, 0.24348505, 0.26158768]\n\ntransform_valid = transforms.Compose([\n    transforms.ToTensor(),\n    transforms.Normalize(CIFAR_MEAN, CIFAR_STD),\n])\nvalid_data = nni.trace(CIFAR10)(root='./data', train=False, download=True, transform=transform_valid)\nvalid_loader = DataLoader(valid_data, batch_size=256, num_workers=6)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<div class=\"alert alert-info\"><h4>Note</h4><p>If you are to use multi-trial strategies, wrapping CIFAR10 with :func:`nni.trace` and\n   use DataLoader from ``nni.nas.evaluator.pytorch`` (instead of ``torch.utils.data``) are mandatory.\n   Otherwise, it's optional.</p></div>\n\nNNI presents many built-in model spaces, along with many *pre-searched models* in :doc:`model space hub </nas/space_hub>`,\nwhich are produced by most popular NAS literatures.\nA pre-trained model is a saved network that was previously trained on a large dataset like CIFAR-10 or ImageNet.\nYou can easily load these models as a starting point, validate their performances, and finetune them if you need.\n\nIn this tutorial, we choose one from `DARTS`_ search space, which is natively trained on our target dataset, CIFAR-10,\nso as to save the tedious steps of finetuning.\n\n.. tip::\n\n   Finetuning a pre-searched model on other datasets is no different from finetuning *any model*.\n   We recommend reading\n   [this tutorial of object detection finetuning](https://pytorch.org/tutorials/intermediate/torchvision_tutorial.html)_\n   if you want to know how finetuning is generally done in PyTorch.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.nas.hub.pytorch import DARTS as DartsSpace\n\ndarts_v2_model = DartsSpace.load_searched_model('darts-v2', pretrained=True, download=True)\n\ndef evaluate_model(model, cuda=False):\n    device = torch.device('cuda' if cuda else 'cpu')\n    model.to(device)\n    model.eval()\n    with torch.no_grad():\n        correct = total = 0\n        for inputs, targets in valid_loader:\n            inputs, targets = inputs.to(device), targets.to(device)\n            logits = model(inputs)\n            _, predict = torch.max(logits, 1)\n            correct += (predict == targets).sum().cpu().item()\n            total += targets.size(0)\n    print('Accuracy:', correct / total)\n    return correct / total\n\nevaluate_model(darts_v2_model, cuda=True)  # Set this to false if there's no GPU."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The journey of using a pre-searched model could end here. Or you are interested,\nwe can go a step further to search a model within :class:`~nni.nas.hub.pytorch.DARTS` space on our own.\n\n## Use the DARTS model space\n\nThe model space provided in `DARTS`_ originated from [NASNet](https://arxiv.org/abs/1707.07012)_,\nwhere the full model is constructed by repeatedly stacking a single computational unit (called a **cell**).\nThere are two types of cells within a network. The first type is called *normal cell*, and the second type is called *reduction cell*.\nThe key difference between normal and reduction cell is that the reduction cell will downsample the input feature map,\nand decrease its resolution. Normal and reduction cells are stacked alternately, as shown in the following figure.\n\n<img src=\"file://../../img/nasnet_cell_stack.png\">\n\nA cell takes outputs from two previous cells as inputs and contains a collection of *nodes*.\nEach node takes two previous nodes within the same cell (or the two cell inputs),\nand applies an *operator* (e.g., convolution, or max-pooling) to each input,\nand sums the outputs of operators as the output of the node.\nThe output of cell is the concatenation of all the nodes that are never used as inputs of another node.\nUsers could read [NDS](https://arxiv.org/pdf/1905.13214.pdf)_ or [ENAS](https://arxiv.org/abs/1802.03268)_ for more details.\n\nWe illustrate an example of cells in the following figure.\n\n<img src=\"file://../../img/nasnet_cell.png\">\n\nThe search space proposed in `DARTS`_ paper introduced two modifications to the original space\nin [NASNet](https://arxiv.org/abs/1707.07012)_.\n\nFirstly, the operator candidates have been narrowed down to seven:\n\n- Max pooling 3x3\n- Average pooling 3x3\n- Skip connect (Identity)\n- Separable convolution 3x3\n- Separable convolution 5x5\n- Dilated convolution 3x3\n- Dilated convolution 5x5\n\nSecondly, the output of cell is the concatenate of **all the nodes within the cell**.\n\nAs the search space is based on cell, once the normal and reduction cell has been fixed, we can stack them for indefinite times.\nTo save the search cost, the common practice is to reduce the number of filters (i.e., channels) and number of stacked cells\nduring the search phase, and increase them back when training the final searched architecture.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>`DARTS`_ is one of those papers that innovate both in search space and search strategy.\n   In this tutorial, we will search on **model space** provided by DARTS with **search strategy** proposed by DARTS.\n   We refer to them as *DARTS model space* (``DartsSpace``) and *DARTS strategy* (``DartsStrategy``), respectively.\n   We did NOT imply that the :class:`~nni.nas.hub.pytorch.DARTS` space and\n   :class:`~nni.nas.strategy.DARTS` strategy has to used together.\n   You can always explore the DARTS space with another search strategy, or use your own strategy to search a different model space.</p></div>\n\nIn the following example, we initialize a :class:`~nni.nas.hub.pytorch.DARTS`\nmodel space, with 16 initial filters and 8 stacked cells.\nThe network is specialized for CIFAR-10 dataset with 32x32 input resolution.\n\nThe :class:`~nni.nas.hub.pytorch.DARTS` model space here is provided by :doc:`model space hub </nas/space_hub>`,\nwhere we have supported multiple popular model spaces for plug-and-play.\n\n.. tip::\n\n   The model space here can be replaced with any space provided in the hub,\n   or even customized spaces built from scratch.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model_space = DartsSpace(\n    width=16,           # the initial filters (channel number) for the model\n    num_cells=8,        # the number of stacked cells in total\n    dataset='cifar'     # to give a hint about input resolution, here is 32x32\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Search on the model space\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>Please set ``fast_dev_run`` to False to reproduce the our claimed results.\n   Otherwise, only a few mini-batches will be run.</p></div>\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "fast_dev_run = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Evaluator\n\nTo begin exploring the model space, one firstly need to have an evaluator to provide the criterion of a \"good model\".\nAs we are searching on CIFAR-10 dataset, one can easily use the :class:`~nni.nas.evaluator.pytorch.Classification`\nas a starting point.\n\nNote that for a typical setup of NAS, the model search should be on validation set, and the evaluation of the final searched model\nshould be on test set. However, as CIFAR-10 dataset doesn't have a test dataset (only 50k train + 10k valid),\nwe have to split the original training set into a training set and a validation set.\nThe recommended train/val split by `DARTS`_ strategy is 1:1.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import numpy as np\nfrom nni.nas.evaluator.pytorch import Classification\nfrom torch.utils.data import SubsetRandomSampler\n\ntransform = transforms.Compose([\n    transforms.RandomCrop(32, padding=4),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize(CIFAR_MEAN, CIFAR_STD),\n])\n\ntrain_data = nni.trace(CIFAR10)(root='./data', train=True, download=True, transform=transform)\n\nnum_samples = len(train_data)\nindices = np.random.permutation(num_samples)\nsplit = num_samples // 2\n\nsearch_train_loader = DataLoader(\n    train_data, batch_size=64, num_workers=6,\n    sampler=SubsetRandomSampler(indices[:split]),\n)\n\nsearch_valid_loader = DataLoader(\n    train_data, batch_size=64, num_workers=6,\n    sampler=SubsetRandomSampler(indices[split:]),\n)\n\nevaluator = Classification(\n    learning_rate=1e-3,\n    weight_decay=1e-4,\n    train_dataloaders=search_train_loader,\n    val_dataloaders=search_valid_loader,\n    max_epochs=10,\n    gpus=1,\n    fast_dev_run=fast_dev_run,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Strategy\n\nWe will use `DARTS`_ (Differentiable ARchiTecture Search) as the search strategy to explore the model space.\n:class:`~nni.nas.strategy.DARTS` strategy belongs to the category of `one-shot strategy <one-shot-nas>`.\nThe fundamental differences between One-shot strategies and `multi-trial strategies <multi-trial-nas>` is that,\none-shot strategy combines search with model training into a single run.\nCompared to multi-trial strategies, one-shot NAS doesn't need to iteratively spawn new trials (i.e., models),\nand thus saves the excessive cost of model training.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>It's worth mentioning that one-shot NAS also suffers from multiple drawbacks despite its computational efficiency.\n   We recommend\n   [Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap](https://arxiv.org/abs/2008.01475)_\n   and\n   [How Does Supernet Help in Neural Architecture Search?](https://arxiv.org/abs/2010.08219)_ for interested readers.</p></div>\n\n:class:`~nni.nas.strategy.DARTS` strategy is provided as one of NNI's :doc:`built-in search strategies </nas/exploration_strategy>`.\nUsing it can be as simple as one line of code.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.nas.strategy import DARTS as DartsStrategy\n\nstrategy = DartsStrategy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. tip:: The ``DartsStrategy`` here can be replaced by any search strategies, even multi-trial strategies.\n\nIf you want to know how DARTS strategy works, here is a brief version.\nUnder the hood, DARTS converts the cell into a densely connected graph, and put operators on edges (see the following figure).\nSince the operators are not decided yet, every edge is a weighted mixture of multiple operators (multiple color in the figure).\nDARTS then learns to assign the optimal \"color\" for each edge during the network training.\nIt finally selects one \"color\" for each edge, and drops redundant edges.\nThe weights on the edges are called *architecture weights*.\n\n<img src=\"file://../../img/darts_illustration.png\">\n\n.. tip:: It's NOT reflected in the figure that, for DARTS model space, exactly two inputs are kept for every node.\n\n### Launch experiment\n\nWe then come to the step of launching the experiment.\nThis step is similar to what we have done in the :doc:`beginner tutorial <hello_nas>`.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.nas.experiment import NasExperiment\n\nexperiment = NasExperiment(model_space, evaluator, strategy)\nexperiment.run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        ".. tip::\n\n   The search process can be visualized with tensorboard. For example::\n\n       tensorboard --logdir=./lightning_logs\n\n   Then, open the browser and go to http://localhost:6006/ to monitor the search process.\n\n   .. image:: ../../img/darts_search_process.png\n\nWe can then retrieve the best model found by the strategy with ``export_top_models``.\nHere, the retrieved model is a dict (called *architecture dict*) describing the selected normal cell and reduction cell.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "exported_arch = experiment.export_top_models(formatter='dict')[0]\n\nexported_arch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The cell can be visualized with the following code snippet\n(copied and modified from [DARTS visualization](https://github.com/quark0/darts/blob/master/cnn/visualize.py)_).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import io\nimport graphviz\nimport matplotlib.pyplot as plt\nfrom PIL import Image\n\ndef plot_single_cell(arch_dict, cell_name):\n    g = graphviz.Digraph(\n        node_attr=dict(style='filled', shape='rect', align='center'),\n        format='png'\n    )\n    g.body.extend(['rankdir=LR'])\n\n    g.node('c_{k-2}', fillcolor='darkseagreen2')\n    g.node('c_{k-1}', fillcolor='darkseagreen2')\n    assert len(arch_dict) % 2 == 0\n\n    for i in range(2, 6):\n        g.node(str(i), fillcolor='lightblue')\n\n    for i in range(2, 6):\n        for j in range(2):\n            op = arch_dict[f'{cell_name}/op_{i}_{j}']\n            from_ = arch_dict[f'{cell_name}/input_{i}_{j}']\n            if from_ == 0:\n                u = 'c_{k-2}'\n            elif from_ == 1:\n                u = 'c_{k-1}'\n            else:\n                u = str(from_)\n            v = str(i)\n            g.edge(u, v, label=op, fillcolor='gray')\n\n    g.node('c_{k}', fillcolor='palegoldenrod')\n    for i in range(2, 6):\n        g.edge(str(i), 'c_{k}', fillcolor='gray')\n\n    g.attr(label=f'{cell_name.capitalize()} cell')\n\n    image = Image.open(io.BytesIO(g.pipe()))\n    return image\n\ndef plot_double_cells(arch_dict):\n    image1 = plot_single_cell(arch_dict, 'normal')\n    image2 = plot_single_cell(arch_dict, 'reduce')\n    height_ratio = max(image1.size[1] / image1.size[0], image2.size[1] / image2.size[0]) \n    _, axs = plt.subplots(1, 2, figsize=(20, 10 * height_ratio))\n    axs[0].imshow(image1)\n    axs[1].imshow(image2)\n    axs[0].axis('off')\n    axs[1].axis('off')\n    plt.show()\n\nplot_double_cells(exported_arch)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<div class=\"alert alert-danger\"><h4>Warning</h4><p>The cell above is obtained via ``fast_dev_run`` (i.e., running only 1 mini-batch).</p></div>\n\nWhen ``fast_dev_run`` is turned off, we get a model with the following architecture,\nwhere you might notice an interesting fact that around half the operations have selected ``sep_conv_3x3``.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_double_cells({\n    'normal/op_2_0': 'sep_conv_3x3',\n    'normal/input_2_0': 1,\n    'normal/op_2_1': 'sep_conv_3x3',\n    'normal/input_2_1': 0,\n    'normal/op_3_0': 'sep_conv_3x3',\n    'normal/input_3_0': 1,\n    'normal/op_3_1': 'sep_conv_3x3',\n    'normal/input_3_1': 2,\n    'normal/op_4_0': 'sep_conv_3x3',\n    'normal/input_4_0': 1,\n    'normal/op_4_1': 'sep_conv_3x3',\n    'normal/input_4_1': 0,\n    'normal/op_5_0': 'sep_conv_3x3',\n    'normal/input_5_0': 1,\n    'normal/op_5_1': 'max_pool_3x3',\n    'normal/input_5_1': 0,\n    'reduce/op_2_0': 'sep_conv_3x3',\n    'reduce/input_2_0': 0,\n    'reduce/op_2_1': 'sep_conv_3x3',\n    'reduce/input_2_1': 1,\n    'reduce/op_3_0': 'dil_conv_5x5',\n    'reduce/input_3_0': 2,\n    'reduce/op_3_1': 'sep_conv_3x3',\n    'reduce/input_3_1': 0,\n    'reduce/op_4_0': 'dil_conv_5x5',\n    'reduce/input_4_0': 2,\n    'reduce/op_4_1': 'sep_conv_5x5',\n    'reduce/input_4_1': 1,\n    'reduce/op_5_0': 'sep_conv_5x5',\n    'reduce/input_5_0': 4,\n    'reduce/op_5_1': 'dil_conv_5x5',\n    'reduce/input_5_1': 2\n})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Retrain the searched model\n\nWhat we have got in the last step, is only a cell structure.\nTo get a final usable model with trained weights, we need to construct a real model based on this structure,\nand then fully train it.\n\nTo construct a fixed model based on the architecture dict exported from the experiment,\nwe can use :func:`nni.nas.space.model_context`. Under the with-context, we will creating a fixed model based on ``exported_arch``,\ninstead of creating a space.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.nas.space import model_context\n\nwith model_context(exported_arch):\n    final_model = DartsSpace(width=16, num_cells=8, dataset='cifar')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We then train the model on full CIFAR-10 training dataset, and evaluate it on the original CIFAR-10 validation dataset.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "train_loader = DataLoader(train_data, batch_size=96, num_workers=6)  # Use the original training data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The validation data loader can be reused.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "valid_loader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We must create a new evaluator here because a different data split is used.\nAlso, we should avoid the underlying pytorch-lightning implementation of :class:`~nni.nas.evaluator.pytorch.Classification`\nevaluator from loading the wrong checkpoint.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "max_epochs = 100\n\nevaluator = Classification(\n    learning_rate=1e-3,\n    weight_decay=1e-4,\n    train_dataloaders=train_loader,\n    val_dataloaders=valid_loader,\n    max_epochs=max_epochs,\n    gpus=1,\n    export_onnx=False,          # Disable ONNX export for this experiment\n    fast_dev_run=fast_dev_run   # Should be false for fully training\n)\n\nevaluator.fit(final_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<div class=\"alert alert-info\"><h4>Note</h4><p>When ``fast_dev_run`` is turned off, we achieve a validation accuracy of 89.69% after training for 100 epochs.</p></div>\n\n## Reproduce results in DARTS paper\n\nAfter a brief walkthrough of search + retrain process with one-shot strategy,\nwe then fill the gap between our results (89.69%) and the results in the `DARTS` paper.\nThis is because we didn't introduce some extra training tricks, including [DropPath](https://arxiv.org/pdf/1605.07648v4.pdf)_,\nAuxiliary loss, gradient clipping and augmentations like [Cutout](https://arxiv.org/pdf/1708.04552v2.pdf)_.\nThey also train the deeper (20 cells) and wider (36 filters) networks for longer time (600 epochs).\nHere we reproduce these tricks to get comparable results with DARTS paper.\n\n\n### Evaluator\n\nTo implement these tricks, we first need to rewrite a few parts of evaluator.\n\nWorking with one-shot strategies, evaluators need to be implemented in the style of `PyTorch-Lightning <lightning-evaluator>`,\nThe full tutorial can be found in :doc:`/nas/evaluator`.\nPutting it briefly, the core part of writing a new evaluator is to write a new LightningModule.\n[LightingModule](https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html)_ is a concept in\nPyTorch-Lightning, which organizes the model training process into a list of functions, such as,\n``training_step``, ``validation_step``, ``configure_optimizers``, etc.\nSince we are merely adding a few ingredients to :class:`~nni.nas.evaluator.pytorch.Classification`,\nwe can simply inherit :class:`~nni.nas.evaluator.pytorch.ClassificationModule`, which is the underlying LightningModule\nbehind :class:`~nni.nas.evaluator.pytorch.Classification`.\nThis could look intimidating at first, but most of them are just plug-and-play tricks which you don't need to know details about.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import torch\nfrom nni.nas.evaluator.pytorch import ClassificationModule\n\nclass DartsClassificationModule(ClassificationModule):\n    def __init__(\n        self,\n        learning_rate: float = 0.001,\n        weight_decay: float = 0.,\n        auxiliary_loss_weight: float = 0.4,\n        max_epochs: int = 600\n    ):\n        self.auxiliary_loss_weight = auxiliary_loss_weight\n        # Training length will be used in LR scheduler\n        self.max_epochs = max_epochs\n        super().__init__(learning_rate=learning_rate, weight_decay=weight_decay, export_onnx=False)\n\n    def configure_optimizers(self):\n        \"\"\"Customized optimizer with momentum, as well as a scheduler.\"\"\"\n        optimizer = torch.optim.SGD(\n            self.parameters(),\n            momentum=0.9,\n            lr=self.hparams.learning_rate,\n            weight_decay=self.hparams.weight_decay\n        )\n        return {\n            'optimizer': optimizer,\n            'lr_scheduler': torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, self.max_epochs, eta_min=1e-3)\n        }\n\n    def training_step(self, batch, batch_idx):\n        \"\"\"Training step, customized with auxiliary loss.\"\"\"\n        x, y = batch\n        if self.auxiliary_loss_weight:\n            y_hat, y_aux = self(x)\n            loss_main = self.criterion(y_hat, y)\n            loss_aux = self.criterion(y_aux, y)\n            self.log('train_loss_main', loss_main)\n            self.log('train_loss_aux', loss_aux)\n            loss = loss_main + self.auxiliary_loss_weight * loss_aux\n        else:\n            y_hat = self(x)\n            loss = self.criterion(y_hat, y)\n        self.log('train_loss', loss, prog_bar=True)\n        for name, metric in self.metrics.items():\n            self.log('train_' + name, metric(y_hat, y), prog_bar=True)\n        return loss\n\n    def on_train_epoch_start(self):\n        # Set drop path probability before every epoch. This has no effect if drop path is not enabled in model.\n        self.model.set_drop_path_prob(self.model.drop_path_prob * self.current_epoch / self.max_epochs)\n\n        # Logging learning rate at the beginning of every epoch\n        self.log('lr', self.trainer.optimizers[0].param_groups[0]['lr'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The full evaluator is written as follows,\nwhich simply wraps everything (except model space and search strategy of course), in a single object.\n:class:`~nni.nas.evaluator.pytorch.Lightning` here is a special type of evaluator.\nDon't forget to use the train/val data split specialized for search (1:1) here.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from nni.nas.evaluator.pytorch import Lightning, Trainer\n\nmax_epochs = 50\n\nevaluator = Lightning(\n    DartsClassificationModule(0.025, 3e-4, 0., max_epochs),\n    Trainer(\n        gpus=1,\n        max_epochs=max_epochs,\n        fast_dev_run=fast_dev_run,\n    ),\n    train_dataloaders=search_train_loader,\n    val_dataloaders=search_valid_loader\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Strategy\n\n:class:`~nni.nas.strategy.DARTS` strategy is created with gradient clip turned on.\nIf you are familiar with PyTorch-Lightning, you might aware that gradient clipping can be enabled in Lightning trainer.\nHowever, enabling gradient clip in the trainer above won't work, because the underlying\nimplementation of :class:`~nni.nas.strategy.DARTS` strategy is based on\n[manual optimization](https://pytorch-lightning.readthedocs.io/en/stable/common/optimization.html)_.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "strategy = DartsStrategy(gradient_clip_val=5.)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Launch experiment\n\nThen we use the newly created evaluator and strategy to launch the experiment again.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>``model_space`` has to be re-instantiated because a known limitation,\n   i.e., one model space instance can't be reused across multiple experiments.</p></div>\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "model_space = DartsSpace(width=16, num_cells=8, dataset='cifar')\n\nexperiment = NasExperiment(model_space, evaluator, strategy)\nexperiment.run()\n\nexported_arch = experiment.export_top_models(formatter='dict')[0]\n\nexported_arch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We get the following architecture when ``fast_dev_run`` is set to False. It takes around 8 hours on a P100 GPU.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_double_cells({\n    'normal/op_2_0': 'sep_conv_3x3',\n    'normal/input_2_0': 0,\n    'normal/op_2_1': 'sep_conv_3x3',\n    'normal/input_2_1': 1,\n    'normal/op_3_0': 'sep_conv_3x3',\n    'normal/input_3_0': 1,\n    'normal/op_3_1': 'skip_connect',\n    'normal/input_3_1': 0,\n    'normal/op_4_0': 'sep_conv_3x3',\n    'normal/input_4_0': 0,\n    'normal/op_4_1': 'max_pool_3x3',\n    'normal/input_4_1': 1,\n    'normal/op_5_0': 'sep_conv_3x3',\n    'normal/input_5_0': 0,\n    'normal/op_5_1': 'sep_conv_3x3',\n    'normal/input_5_1': 1,\n    'reduce/op_2_0': 'max_pool_3x3',\n    'reduce/input_2_0': 0,\n    'reduce/op_2_1': 'sep_conv_5x5',\n    'reduce/input_2_1': 1,\n    'reduce/op_3_0': 'dil_conv_5x5',\n    'reduce/input_3_0': 2,\n    'reduce/op_3_1': 'max_pool_3x3',\n    'reduce/input_3_1': 0,\n    'reduce/op_4_0': 'max_pool_3x3',\n    'reduce/input_4_0': 0,\n    'reduce/op_4_1': 'sep_conv_3x3',\n    'reduce/input_4_1': 2,\n    'reduce/op_5_0': 'max_pool_3x3',\n    'reduce/input_5_0': 0,\n    'reduce/op_5_1': 'skip_connect',\n    'reduce/input_5_1': 2\n})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Retrain\n\nWhen retraining,\nwe extend the original dataloader to introduce another trick called [Cutout](https://arxiv.org/pdf/1708.04552v2.pdf)_.\nCutout is a data augmentation technique that randomly masks out rectangular regions in images.\nIn CIFAR-10, the typical masked size is 16x16 (the image sizes are 32x32 in the dataset).\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "def cutout_transform(img, length: int = 16):\n    h, w = img.size(1), img.size(2)\n    mask = np.ones((h, w), np.float32)\n    y = np.random.randint(h)\n    x = np.random.randint(w)\n\n    y1 = np.clip(y - length // 2, 0, h)\n    y2 = np.clip(y + length // 2, 0, h)\n    x1 = np.clip(x - length // 2, 0, w)\n    x2 = np.clip(x + length // 2, 0, w)\n\n    mask[y1: y2, x1: x2] = 0.\n    mask = torch.from_numpy(mask)\n    mask = mask.expand_as(img)\n    img *= mask\n    return img\n\ntransform_with_cutout = transforms.Compose([\n    transforms.RandomCrop(32, padding=4),\n    transforms.RandomHorizontalFlip(),\n    transforms.ToTensor(),\n    transforms.Normalize(CIFAR_MEAN, CIFAR_STD),\n    cutout_transform,\n])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "The train dataloader needs to be reinstantiated with the new transform.\nThe validation dataloader is not affected, and thus can be reused.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "train_data_cutout = nni.trace(CIFAR10)(root='./data', train=True, download=True, transform=transform_with_cutout)\ntrain_loader_cutout = DataLoader(train_data_cutout, batch_size=96)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We then create the final model based on the new exported architecture.\nThis time, auxiliary loss and drop path probability is enabled.\n\nFollowing the same procedure as paper, we also increase the number of filters to 36, and number of cells to 20,\nso as to reasonably increase the model size and boost the performance.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "with model_context(exported_arch):\n    final_model = DartsSpace(width=36, num_cells=20, dataset='cifar', auxiliary_loss=True, drop_path_prob=0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We create a new evaluator for the retraining process, where the gradient clipping is put into the keyword arguments of trainer.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "max_epochs = 600\n\nevaluator = Lightning(\n    DartsClassificationModule(0.025, 3e-4, 0.4, max_epochs),\n    trainer=Trainer(\n        gpus=1,\n        gradient_clip_val=5.,\n        max_epochs=max_epochs,\n        fast_dev_run=fast_dev_run\n    ),\n    train_dataloaders=train_loader_cutout,\n    val_dataloaders=valid_loader,\n)\n\nevaluator.fit(final_model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "When ``fast_dev_run`` is turned off, after retraining, the architecture yields a top-1 accuracy of 97.12%.\nIf we take the best snapshot throughout the retrain process,\nthere is a chance that the top-1 accuracy will be 97.28%.\n\n<img src=\"file://../../img/darts_val_acc.png\">\n\nIn the figure, the orange line is the validation accuracy curve after training for 600 epochs,\nwhile the red line corresponding the previous version in this tutorial before adding all the training tricks and\nonly trains for 100 epochs.\n\nThe results outperforms \"DARTS (first order) + cutout\" in `DARTS`_ paper, which is only 97.00\u00b10.14%.\nIt's even comparable with \"DARTS (second order) + cutout\" in the paper (97.24\u00b10.09%),\nthough we didn't implement the second order version.\nThe implementation of second order DARTS is in our future plan, and we also welcome your contribution.\n\n"
      ]
    }
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